Typical surveillance cameras, when installed in their intended orientation, have a field-of-view (FOV) with a wider horizontal dimension than a vertical dimension. Thus, typical surveillance cameras capture and generate video image frames in an aspect ratio that matches the wide horizontal FOV, such as in 5:4, 4:3, 3:2, or 16:9 width-to-height aspect ratios or other aspect ratios having a larger width (horizontal dimension) than height (vertical dimension). However, for some surveillance applications, a FOV having a larger vertical dimension than the horizontal dimension may be beneficial, such as when monitoring foot and/or vehicle traffic up and down a deep corridor, a long sidewalk, or a long road.
In such applications, much of the FOV on the horizontal dimension may be wasted (e.g., not capturing any meaningful portions of a monitored scene), while the shorter vertical dimension may not be sufficient for viewing down a long corridor, sidewalk, or road, for example. Even if these surveillance cameras are mounted not in their normal orientation but on their sides to capture more vertical area of a scene, the captured video image frames would not correspond to the rotated FOV and thus would be difficult for users to interpret. Thus, there is a need for improved analytics techniques for surveillance camera that takes into account the orientation of the camera.